Training world models needs egocentric video and dense action signals, synchronized. That data is genuinely hard to find.
We built it from Counter-Strike 2 demos.
CS2-10k: 600K+ player-round videos, 10K+ hours, per-frame annotations — keyboard state, mouse delta, 3D position, camera yaw/pitch. All paired to the visual stream.
Why CS2 demos? Matches are deterministic replays. We can reconstruct first-person video and extract the exact control inputs that caused every visual change. No labeling, no estimation.
We're also releasing cs2-dem-renderer — the open-source pipeline we used to build it. Give it a .dem file, it outputs .mp4 + .parquet. Run your own dataset at whatever scale you need.
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